GNN-PMB: A Simple but Effective Online 3D Multi-Object Tracker without Bells and Whistles

TL;DR

This paper introduces GNN-PMB, a straightforward yet effective online 3D multi-object tracker based on RFS theory, demonstrating superior performance over many existing methods in autonomous driving datasets.

Contribution

Proposes the GNN-PMB tracker, combining RFS-based Bayesian filtering with simplicity, and provides a systematic comparison showing its competitive edge in 3D MOT tasks.

Findings

GNN-PMB outperforms most state-of-the-art LiDAR-only trackers.

It ranks 3rd on the nuScenes 3D tracking leaderboard.

The RFS-based approach is demonstrated to be superior to traditional vector-based filters.

Abstract

Multi-object tracking (MOT) is among crucial applications in modern advanced driver assistance systems (ADAS) and autonomous driving (AD) systems. The global nearest neighbor (GNN) filter, as the earliest random vector-based Bayesian tracking framework, has been adopted in most of state-of-the-arts trackers in the automotive industry. The development of random finite set (RFS) theory facilitates a mathematically rigorous treatment of the MOT problem, and different variants of RFS-based Bayesian filters have then been proposed. However, their effectiveness in the real ADAS and AD application is still an open problem. In this paper, it is demonstrated that the latest RFS-based Bayesian tracking framework could be superior to typical random vector-based Bayesian tracking framework via a systematic comparative study of both traditional random vector-based Bayesian filters with rule-based heuristic track maintenance and RFS-based Bayesian filters on the nuScenes validation dataset. An RFS-based tracker, namely Poisson multi-Bernoulli filter using the global nearest neighbor (GNN-PMB), is proposed to LiDAR-based MOT tasks. This GNN-PMB tracker is simple to use, and it achieves competitive results on the nuScenes dataset. Specifically, the proposed GNN-PMB tracker outperforms most state-of-the-art LiDAR-only trackers and LiDAR and camera fusion-based trackers, ranking the $3^{rd}$ among all LiDAR-only trackers on nuScenes 3D tracking challenge leader board at the time of submission.